Fracture detection using monopole source in acoustic well logging

ABSTRACT

The present disclosure relates generally to well logging and measurement in subterranean formations and more particularly, the present disclosure relates to a system and method for identifying anisotropic formations, such as fractures and fracture patterns, in subterranean formations. The method uses waveforms transmitted from a monopole source. After finding the Root Mean Square energy from at least four quadrants, the energy data is normalized. At least one minimum RMS energy point is identified and the azimuth of that minimum is also identified. The azimuth and the minimum are instructive in defining fracture patterns and characteristics.

CROSS REFERENCE TO RELATED APPLICATIONS

The present disclosure claims priority to provisional patent application61/816,992.

BACKGROUND

The present disclosure relates generally to well logging and measurementin subterranean formations and more particularly, the present disclosurerelates to a system and method for identifying anisotropic formations,such as fractures and fracture patterns, in subterranean formations.

Acoustic well-logging may be used to determine fracture patterns oranomalies in subterranean formations. Orthogonal acoustic wavepropagation and its polarization are used to determine fracturedirection—also known as “seismic anisotropy.” Industry professionals mayuse this information for hydrocarbon or mineral extraction, for instanceand while fractures can indicate hydrocarbon or mineral deposits, notall anisotropic formations function as indicators of deposits.Furthermore, fracture direction may provide essential information on thedirection and extent of drilling for extraction of materials fromsubterranean formations.

Historically, seismic anisotropy technology used refraction ofcompressional wave propagation to determine the azimuth of the fracture.The compressional wave would refract in a chevron pattern when hittingan anisotropic subterranean formation. The refraction, however, occurswhen encountering any anisotropic formation, not just fracture-relatedanisotropic formations. Furthermore, the historical method relies onwavelength for finding the fracture. The wavelength of compressionalwaves is considered “long”—it is often referred to as a “slowwave”—thereby limiting the size of the fracture or anisotropic formationdetectable to larger fractures. Due to the ambiguity and the lack ofprecision available from use of the historical method, it was abandonedfor newer technology.

Currently, seismic anisotropy technology uses a method that interpretsshear wave propagation to determine the azimuth of the fracture. Inanisotropic formations, shear waves split into fast and slow componentsthat move in orthogonal directions. The difference between the fast andslow components indicates the degree of anisotropy. Fractures areanisotropic, so shear wave splitting can be an indication of fracturesin subterranean formations; however, not all anisotropic formations arefractures. Therefore, use of shear wave anisotropy is not so much adefinitive indicator of fractures, but a definitive indicator ofanisotropic formations that may be fractures but may be alternativeanisotropic, or seemingly anisotropic, formations. This demonstratessome key limitations to the use of shear wave anisotropy that arebecoming more pronounced as hydrocarbon extraction involves moreanomalous and tighter subterranean formations. Furthermore, fractures,though anisotropic, can also exist in mildly anisotropic formations thatcannot be detected by the present-day methods, as it is difficult todistinguish the subtle fracture patterns from the surroundinganisotropic media using shear waves.

In situations where energy mapping from shear wave anisotropy fails todistinguish between surrounding rock formations or where a redundantmethod to verify the shear wave results is asked for, shear sonicimaging is sometimes used to identify anisotropic formations insurrounding subterranean formations. The imaging technology relies on adifference of resistivity between the fluid in the well bore and thesurrounding rock formations. The larger the difference in resistivity,the clearer the image produced should be. With softer rock formations,such as shale, the resistivity between the rock and the fluid in thewell bore is not significantly different. Therefore, the current imagingsoftware may not be able to provide clear pictures of anisotropicformations when used on soft rock formations.

Due to the drawbacks of the current and past methods, there still existsa need in the market for a method or technology to address theseconcerns and to improve the overall efficiency of fractureidentification in subterranean formations.

SUMMARY

The present disclosure provides a method for identifying characteristicsof subterranean formations that could increase the efficiency of andlessen the environmental impact of hydrocarbon extraction. Oneembodiment of the present disclosure can be a computer program with amultitude of steps that allow the user to identify characteristics ofsubterranean formations, such as fracture patterns and the direction ofthe fracture or fractures found. An alternative embodiment could simplybe a set of steps and calculations leading to the identification ofcharacteristics of subterranean formations, such as fractures orfracture patterns and the directions of those fractures. However, theseembodiments should not be construed as limitations on the scope of anyembodiment, but as exemplifications of various embodiments thereof. Manyother variations are possible within the teachings of the variousembodiments. Thus, the scope should be determined by the appended claimsand their equivalents, and not by the examples given.

BRIEF DESCRIPTION OF FIGURES

The following detailed description of embodiments of the invention willbe better understood when read in conjunction with the accompanyingdrawings. For the purpose of illustrating the invention, there is shownin the drawings, embodiments which are presently preferred. In thedrawings, the left-most digit(s) of a reference number indicates thedrawing in which the reference number first appears. The same referencenumbers have been used throughout the drawings to indicate similarelements of the drawings.

FIG. 1 illustrates a raw monopole waveform data from a receiver 1 inQuadrant 1, in accordance with an embodiment of the present invention;

FIG. 2 illustrates an example of receiver move outs from receiver 1 inQuadrant 1, in accordance with the embodiment of the present invention;

FIG. 3 illustrates the receiver data shown in FIG. 1, tracked it intime, in accordance with the embodiment of the present invention;

FIG. 4 illustrates a frequency of spectrum of raw monopole waveform, inaccordance with the embodiment of the present invention;

FIG. 5 illustrates the monopole data muted in time and frequencyfiltered, in accordance with the embodiment of the present invention;

FIG. 6 illustrates Raw RMS Energy Output from Receivers in Quadrants1-4, in accordance with the embodiment of the present invention;

FIG. 7 illustrates normalized RMS energy from receivers in Quadrants1-4, in accordance with the embodiment of the present invention;

FIG. 8 illustrates a homogenous interval on right side and anon-homogenous interval on left side, in accordance with the embodimentof the present invention;

FIG. 9 illustrates comparison of sonic image of a seismic fracture onthe left to the normalized RMS compression wave data on the right, inaccordance with the embodiment of the present invention;

FIG. 10 illustrates fracture detection from P-waves Azimuth Comparisonwith Image, in accordance with the embodiment of the present invention;and

FIG. 11 illustrates steps of determining anisotropic characteristics insubterranean formations, in accordance with the embodiment of thepresent invention.

DETAILED DESCRIPTION

For a thorough understanding of the present disclosure, reference is tobe made to the following detailed description in connection with theabove-mentioned drawings. Although the present disclosure is describedin connection with exemplary embodiment, the present disclosure is notintended to be limited to the specific forms set forth herein. It isunderstood that various omissions and substitutions of equivalents arecontemplated as circumstances may suggest or render expedient, but theseare intended to cover the application or implementation withoutdeparting from the spirit or scope of the present disclosure. Further,it will nevertheless be understood that no limitation in the scope ofthe disclosure is thereby intended, such alterations and furthermodifications in the figures and such further applications of theprinciples of the disclosure as illustrated therein being contemplatedas would normally occur to one skilled in the art to which thedisclosure relates. Also, it is to be understood that the phraseologyand terminology used herein is for the purpose of description and shouldnot be regarded as limiting. Further, reference herein to “oneembodiment” or “an embodiment” means that a particular feature,characteristic, or function described in connection with the embodimentis included in at least one embodiment of the disclosure. Furthermore,the appearances of such phrase at various places herein are notnecessarily all referring to the same embodiment. The terms “a” and “an”herein do not denote a limitation of quantity, but rather denote thepresence of at least one of the referenced item.

The present disclosure provides a method for finding fractures orfracture patterns in subterranean formations that takes full advantageof the sensitivity of modern technology and creates an opportunity formore effective, efficient, and less expensive hydrocarbon extraction.For illustrative purposes, the present disclosure will solely speak tohydrocarbon extraction. The extraction of hydrocarbons from subsurfaceformations commences by drilling a borehole through the earth to atarget depth considered to be hydrocarbon bearing. Finding and producinghydrocarbons efficiently and effectively requires understanding thecharacteristics of the geologic properties of subterranean formations.The more accurate the information about the anisotropic formations, themore accurately drilling for hydrocarbon extraction can occur. Itprovides information on scope of the area of extraction operations, thedirection of extraction operations, it could lower costs of theoperation, and it could create less environmental exposure to chemicals.Mainly three measurements—electromagnetic, nuclear and acoustic—havebeen devised to achieve this end. The present disclosure relates toacoustic, or sonic, wave measurements.

Acoustic waves may be transmitted or propagated by several ways, but aremainly propagated using monopole or dipole sources. The propagated wavescan be received, or logged, using a multitude of receivers located infour quadrants. The data received is considered to be raw acousticdata—raw because it has not yet been processed. FIG. 1 shows an exampleof raw monopole waveform data. In FIG. 1 the waveforms show reasonableleading noise; in particular, the waveforms shown deteriorate after twocycles. The leading noise should be eliminated to properly process thesignal, but may sometimes be partially retained to ensure a large enoughsample of data. The point of deterioration should be zeroed in laterprocessing steps. After receipt of the raw data, processing orsubsequent computations can occur.

As mentioned, more than one receiver logs the raw data for processing.These receivers are placed at a variety of distances from the monopolesource. FIG. 2 shows an example of the monopole waveform recorded atdifferent distances from the source—also known as the “move-out.” Somereceivers may fall out of phase for any variety of reasons, includingtool shock or lack of maintenance. Specifically in FIG. 2, receivers 11,12, and 13 show waveforms not in phase with the remainder of receivers.Using all of the receivers' data for processing anisotropy could lead toerrors because the out of phase receivers could distort your image.Therefore, processing should use the in-phase receivers for subsequentprocessing steps.

FIG. 3 shows the same receiver data as seen in FIG. 1, but tracked intime. In this step, some of the noisy data was retained to ensure thatenough data points—a large enough sample—were kept to compute the RootMean Square (RMS) energy. Some of the noise can be cleaned up in thefrequency domain as a filtering step or it may be necessary to redo theprocessing steps eliminating all noisy data if the results do not alignwith expected results.

After tracking the raw data in time, the frequency of the raw monopolewaveform is calculated using a Fourier transform. An example of theresult of a frequency calculation is shown in FIG. 4. The results helpto determine the perimeters for frequency filtering because theremaining processing steps can be focused on the area where the energyis concentrated. In FIG. 4, the majority of the energy is between 5-8kHz so subsequent processing steps would only use data from betweenthose perimeters in that example. The data points outside of thedetermined parameters are filtered out of the frequency domain. Thefiltered waveforms are converted back to a time series using a reverseFourier transform. FIG. 5 shows the data now that it has been filteredand converted back into a time series—also known as data muted in thetime and frequency domains.

FIG. 6 shows the waveform data from four separate quadrants. Themonopole pulse sends at least four waveforms recorded at the same depth,but 90 degrees, or orthogonal, from one another. The Root Mean Square(RMS) should be calculated for each of these monopole waveforms as shownbelow. FIG. 6 shows the RMS energy output from four orthogonal waveformsoverlaying one another. However, the RMS energy output is clearlymismatched, in this example and in most data sets received, so the RMSenergy should be normalized or averaged. FIG. 7 shows the data afternormalization.

RMS energy can be computed as shown:

RMS Computation (my $i = $start_col; $i <= $end_col; $i++) {  $sum2 +=$line[$i] * $line[$i]; } my $average = $sum2 / $NPTS; my $rms_value =sqrt($average); Azimuth and RMS using Modified Alford $sum2 = 0; for (my$i=0; $i < $npts; $i++) { my $radian = deg2rad($theta); my $cos2theta =2*(cos($radian)*cos($radian))−1; my $sin2theta =2*sin($radian)*cos($radian); my $costh_sinth = cos($radian) *sin($radian); my $xx = int(−$xx_data[$i] * $sin2theta +  $yy_data[$i] *$sin2theta + $xy_data[$i] * $cos2theta+ $yx_data[$i] * $cos2theta); my$yy = int($xx_data[$i] * $sin2theta −  $yy_data[$i] * $sin2theta −$xy_data[$i] * $cos2theta − $yx_data[$i] * $cos2theta); my $xy = int(−$xx_data[$i] * $costh_sinth +  $yy_data[$i] * $costh_sinth −$xy_data[$i] * $sin2theta + $yx_data[$i] * $cos2theta); my $yx = int(−$xx_data[$i] * $costh_sinth +  $yy_data[$i] * $costh_sinth +$xy_data[$i] * $cos2theta − $yx_data[$i] * $sin2theta); # # Compute RMSamplitude for this rotated time-series and # $sum2 += $yy*$yy+$xx*$xx+$xy*$xy+$yx*$yx; Four time series to be combined and rotatedare: XX, XY, YY and YX

From the normalized RMS energy output, the particularly important datapoints are the minimum RMS energy found and the angle at which thatminimum is found. The minimum should demonstrate where the fractureexists if there is a fracture present. The angle at which the minimum isfound demonstrates the azimuth, or angle, of the fracture. The RMSenergy minimum and the angle of the normalized data, then, provideinformation for where and in which direction to drill for hydrocarbonextraction. If there is no fracture present, then the difference betweenthe minimum and the normalized data will be negligible. When there is afracture, however, there will be a significant difference shown. In FIG.8, on the right, there is an example of a homogenous interval, aninterval where there is no fracture, and on the left, there is anexample of a non-homogenous interval, an interval where there is afracture. FIG. 8 shows the sharp drop in energy that can be seen in thepresence of a fracture with the dotted blue line near 12,900 feet.

For certain subterranean formations, sonic imaging tools are used inaddition to or instead of collecting acoustic wave data. FIG. 9 shows asonic image of a subterranean formation with a fracture compared to anenergy map using the present disclosure's process. There is a drop inRMS energy that coincides with the fracture shown in the image. Thefractures seen by the sonic images, therefore, can also be seen by thepresent disclosure's processing method. Furthermore, there is a higherdensity of anisotropic formations seen by the present disclosure thancomparable methods.

FIG. 11 describes the method for determining anisotropic characteristicsin subterranean formations in a borehole

-   At S1 the process starts;-   At S2 raw monopole acoustic data from a tool is filtered;-   At S3 the Root Mean Square energy is calculated from the filtered    raw acoustic data;-   At S4 the Root Mean Square energy that has been calculated is    normalized;-   At S5 at least one minimum is found in the normalized Root Mean    Square energy;-   At S6 the minimum or minimums are compared to the baseline or    average normalized Root Mean Square energy for a not negligible    difference to identify the existence of fracture or fractures;-   At S7 the angle of the minimum or minimums is identified to    determine fracture characteristics; and-   At S8 the process ends.

While embodiments of this disclosure have been depicted and describedand are defined by reference to exemplary embodiments of the disclosure,such references do not imply a limitation on the disclosure, and no suchlimitation is to be inferred. The subject matter disclosed is capable ofconsiderable modification, alteration, and equivalents in form andfunction, as will occur to those skilled in the pertinent art and havingthe benefit of this disclosure. The depicted and described embodimentsof this disclosure are examples only, and not exhaustive of the scope ofthe disclosure. For example, an alternative embodiment of the currentdisclosure may also be a computer program with automates the aboveprocessing steps.

Increasing the accuracy of the current processing technology would allowcorporations to extract the same amount of hydrocarbon as currentoperations allow from a smaller area, thereby lowering the cost ofhydrocarbon extraction. Furthermore, extraction from a smaller areawould require fewer chemicals and less potential for leaking thosechemicals into groundwater of populated areas. The capability ofstreamlining and reducing the impact of hydrocarbon extraction wouldprovide leverage in environmental, societal, and political discussions.

Providing a process that is more sensitive to anisotropic formations andthat can be used on softer subterranean formations, would enlarge thescope of acoustic processing capabilities. Enlarging the scope ofprocessing capabilities while also increasing the accuracy of thatprocessing would provide more information on which direction to directextraction operations and on how large of an area to direct extractionoperations.

An example of a method for determining fracture patterns andcharacteristics in subterranean formations is described. Thetransmission and receipt of acoustic waves, though necessary, happensprior to the first step of the current method. Specific tools alreadyused for hydrocarbon extraction provide the raw acoustic data bytransmitting and collecting the said raw acoustic data. The presentdisclosure can take advantage of said existing tools and provides a newmethod for interpreting and processing the raw data collected. The rawacoustic data can first be filtered to eliminate data that does notensure an accurate sample. A Root Mean Square energy from the acousticdata is calculated and normalized. A minimum Root Mean Square energyfrom the normalized data is identified and the angle of the minimum RMSenergy is also noted.

Preferred embodiments are described herein, including the best modeknown to the inventor. Of course, variations of those preferredembodiments will become apparent to those of ordinary skill in the artupon reading the foregoing description. The inventor expects skilledartisans to employ such variations as appropriate. Moreover, anycombination of the above-described elements in all possible variationsthereof is encompassed unless otherwise indicated herein or otherwiseclearly contradicted by context.

What is claimed is:
 1. A method for determining anisotropiccharacteristics in subterranean formations, the method comprising stepsof: filtering raw acoustic data from a tool; calculating Root MeanSquare energy from said raw acoustic data; normalizing the Root MeanSquare energy; finding at least one minimum Root Mean Square energy fromthe normalized data; identifying angle of said minimum Root Mean Squareenergy; and using said minimum Root Mean Square energy and said angle toidentify anisotropic characteristics.
 2. The method as claimed in claim1 wherein said tool propagated acoustic waves from a monopole source 3.The method as claimed in claim 2 wherein the said raw acoustic data iscollected in at least four quadrants
 4. The method as claimed in claim 3wherein the said raw acoustic data is filtered in the time and frequencydomains before calculating the Root Mean Square energy.
 5. The method asclaimed in claim 3 wherein the said raw acoustic data is filtered in thetime domain before calculating the Root Mean Square energy.
 6. Themethod as claimed in claim 3 wherein the said raw acoustic data isfiltered in the frequency domain before calculating the Root Mean Squareenergy.
 7. A method for determining anisotropic characteristics insubterranean formations, the method comprising steps of: filtering rawacoustic data from a tool; calculating Root Mean Square energy from saidraw acoustic data; normalizing the Root Mean Square energy; finding atleast one minimum Root Mean Square energy from normalized data;identifying angle of said minimum Root Mean Square energy; and usingsaid minimum Root Mean Square energy and said angle to identifyanisotropic characteristics.
 8. The method as claimed in claim 7 whereinsaid tool propagated acoustic waves from a monopole source
 9. The methodas claimed in claim 8 wherein the said raw acoustic data is collected inat least four quadrants
 10. The method as claimed in claim 9 wherein thesaid minimum Root Mean Square energy is compared to the average RootMean Square energy to determine anisotropic characteristics.
 11. Themethod as claimed in claim 10 wherein the said raw acoustic data isfiltered in the time and frequency domains before calculating the RootMean Square energy.
 12. The method as claimed in claim 10 wherein thesaid raw acoustic data is filtered in the time domain before calculatingthe Root Mean Square energy.
 13. The method as claimed in claim 10wherein the said raw acoustic data is filtered in the frequency domainbefore calculating the Root Mean Square energy
 14. A method fordetermining anisotropic characteristics in subterranean formations, themethod comprising steps of: collecting raw acoustic data from a monopolesource; filtering said raw acoustic data; calculating Root Mean Squareenergy from the said raw acoustic data; normalizing the Root Mean Squareenergy; finding at least one minimum Root Mean Square energy from thenormalized data; finding difference between said minimum Root MeanSquare energy and baseline Root Mean Square data to identify theexistence of anisotropic characteristics; and identifying the angle ofsaid minimum Root Mean Square energy to determine anisotropiccharacteristics.
 15. The method as claimed in claim 14 wherein saidanisotropic characteristics in subterranean formations are fractures.16. A method of identifying fracture characteristics in subterraneanformations comprising steps of: calculating Root Mean Square energy fromraw acoustic data collected from four quadrants; normalizing said RootMean Square energy; finding at least one minimum Root Mean Square energyfrom said normalized Root Mean Square energy comparing said minimum RootMean Square energy to baseline Root Mean Square energy to determine theexistence of fractures in subterranean formations; and identifying theangle of said minimum Root Mean Square energy to determine fracturecharacteristics.
 17. A method of identifying fracture characteristics insubterranean formations comprising steps of: filtering raw acoustic datacollected from a monopole source; calculating Root Mean Square energyfrom said raw acoustic data; normalizing said Root Mean Square energy;finding at least one minimum Root Mean Square energy from saidnormalized Root Mean Square energy comparing said minimum Root MeanSquare energy to baseline Root Mean Square energy to determine theexistence of fractures in subterranean formations; and identifying theangle of said minimum Root Mean Square energy to determine fracturecharacteristics.
 18. The method as claimed in claim 17 wherein said rawacoustic data is collected from at least four quadrants.
 19. The methodas claimed in claim 18, wherein said raw acoustic data is filtered intime.
 20. The method as claimed in claim 18 wherein said raw acousticdata is filtered in frequency.
 21. The method as claimed in claim 18wherein said raw acoustic data is filtered in frequency and time.
 22. Amethod of identifying fracture characteristics in subterraneanformations comprising steps of: filtering raw acoustic data collectedfrom a monopole source; calculating Root Mean Square energy from saidraw acoustic data; normalizing said Root Mean Square energy; finding atleast one minimum Root Mean Square energy from said normalized Root MeanSquare energy comparing said minimum Root Mean Square energy to baselineRoot Mean Square energy to determine the existence of fractures insubterranean formations; and identifying the angle of said minimum RootMean Square energy to determine fracture characteristics.
 23. A methodof identifying fracture characteristics in subterranean formationscomprising steps of: filtering raw acoustic data collected from at leastfour quadrants; calculating Root Mean Square energy from said rawacoustic data; normalizing said Root Mean Square energy; finding atleast one minimum Root Mean Square energy from said normalized Root MeanSquare energy comparing said minimum Root Mean Square energy to baselineRoot Mean Square energy to determine the existence of fractures insubterranean formations; and identifying the angle of said minimum RootMean Square energy to determine fracture characteristics.
 24. A methodof identifying fracture characteristics in subterranean formationscomprising steps of: filtering raw monopole acoustic data collected fromat least four quadrants; calculating Root Mean Square energy from saidraw acoustic data; normalizing said Root Mean Square energy; finding atleast one minimum Root Mean Square energy from said normalized Root MeanSquare energy comparing said minimum Root Mean Square energy to baselineRoot Mean Square energy to determine the existence of fractures insubterranean formations; and identifying the angle of said minimum RootMean Square energy to determine fracture characteristics.
 25. A methodas claimed in claim 24, wherein, considering comparison of said minimumRoot Mean Square energy to baseline Root Mean Square energy, involvesdetermining a not negligible difference.